Deutsches Institut für
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SOEPpapers
on Multidisciplinary Panel Data Research
167
Michael Naef • Jürgen Schupp
K
Measuring Trust:
Experiments and Surveys in Contrast and Combination
Berlin, March 2009
SOEPpapers on Multidisciplinary Panel Data Research
at DIW Berlin
This series presents research findings based either directly on data from the German SocioEconomic Panel Study (SOEP) or using SOEP data as part of an internationally comparable
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Editors:
Georg Meran (Dean DIW Graduate Center)
Gert G. Wagner (Social Sciences)
Joachim R. Frick (Empirical Economics)
Jürgen Schupp (Sociology)
Conchita D’Ambrosio (Public Economics)
Christoph Breuer (Sport Science, DIW Research Professor)
Anita I. Drever (Geography)
Elke Holst (Gender Studies)
Frieder R. Lang (Psychology, DIW Research Professor)
Jörg-Peter Schräpler (Survey Methodology)
C. Katharina Spieß (Educational Science)
Martin Spieß (Survey Methodology)
Alan S. Zuckerman (Political Science, DIW Research Professor)
ISSN: 1864-6689 (online)
German Socio-Economic Panel Study (SOEP)
DIW Berlin
Mohrenstrasse 58
10117 Berlin, Germany
Contact: Uta Rahmann | urahmann@diw.de
Measuring Trust:
Experiments and Surveys in Contrast and Combination
Michael Naef and Jürgen Schupp
∗
†
March 2009
Abstract
Trust is a concept that has attracted - significant attention in economic theory and
research within the last two decades: it has been applied in a number of contexts and
has been investigated both as an explanatory and as a dependent variable. In this
paper, we explore the questions of what exactly is measured by the diverse surveyderived scales and experiments claiming to measure trust, and how these different
measures are related. Using nationally representative data, we test a commonly used
experimental measure of trust for robustness to a number of interferences, finding it
to be mostly unsusceptible to stake size, the extent of strategy space, the use of the
strategy method, and the characteristics of the experimenters. Inspired by criticism
of the widespread trust question used in many surveys, we created a new, improved
survey trust scale consisting of three short statements. We show that the dimension
of this scale is distinct from trust in institutions and trust in known others. Our new
scale is a valid and reliable measure of trust in strangers. The scale is valid in the
sense that it correlates with trusting behaviour in the experiment. Furthermore, we
demonstrate that the test-retest reliability of six weeks is high. The experimental
measure of trust is, on the other hand, not significantly correlated with trust in
institutions nor with trust in known others. We therefore conclude that the experimental measure of trust refers not to trust in a general sense, but specifically to
trust in strangers.
Keywords: Trust, Experiment, Survey, Representativity, SOEP
JEL Classifications: C83, C91, D63 and Z13
∗
We want to thank Ernst Fehr, Urs Fischbacher and Gert G. Wagner with who we designed the
survey, for their contribution and helpful comments; Andreas Stocker and Bernhard von Rosenbladt for
their support in finding practical solutions for the field work of the surveys; and Deborah Anne Bowen
for her critical review of the manuscript.
†
Michael Naef: Department of Economics, Royal Holloway, University of London, Egham TW20 0EX,
U.K., michael.naef@rhul.ac.uk; Jürgen Schupp: Socio-Economic Panel Study (SOEP), DIW Berlin, D10108 Berlin, jschupp@diw.de
1
1
Introduction
In surveys like the General Social Survey (GSS) or the World Values Survey (WVS), trust
is measured with the statement ”Generally speaking, would you say that most people can
be trusted or that you can’t be too careful in dealing with people?” This measure of trust
has been criticized and its behavioural relevance has been called into question.
The first systematic study of the relation between survey and behavioural measures
of trust was reported by Glaeser et al. (2000). They investigated whether behaviour
in a trust game is correlated with this standard survey measure of trust. They find
that the above question is not correlated with trusting behaviour. This result has been
replicated in several other studies (e.g., Ashraf et al. 2003, Ermisch et al. 2007, Gachter
et al. 2004, Haile et al. 2008, Holm & Nystedt 2008, Johansson-Stenman et al. 2005b).
However, other studies have found a significant correlation between the survey and the
experimental measures (e.g., Vyrastekova & Garikipati 2005, Bellemare & Kroeger 2007,
Sapienza et al. 2007). Based on the previous research one cannot conclude whether the
GSS question is behaviourally relevant in the sense that it correlates with the behaviour
in the trust game.
What are the reasons for these conflicting results? Are the experiment and the survey
measures both valid and reliable measures of trust? Concerning survey measures, several
studies have revealed that the GSS question is neither a valid nor a reliable measure
of trust (Reeskens & Hooghe 2008). The question is rather imprecise, the possible
answers are not mutually exclusive, and only one item is not considered to be a reliable
measurement (e.g., Glaeser et al. 2000, Miller & Mitamura 2003, Yamagishi et al. 1999).
Concerning the experimental measure, little is known about its sensitivity and validity
in large and heterogeneous populations.1 Against this background, it is no surprise that
there is no clear relationship between survey and experimental measures of trust.
The aim of this article is to connect the survey measures and the experimental
measures of trust. In particular, we would like to show that survey and experimental
1
Most experiments are run with students as subjects
2
measures can be connected in a large representative survey. Since the experimental
measure might capture a very specific dimension of trust, we created a new survey
measure of trust that, on the one hand, takes recent criticisms of the GSS question into
account, and on the other, measures the same dimension of trust as the experiment.
To avoid confusion we have to clarify first what we mean by the word ”trust”. We
largely follow James Coleman’s concept of trust (Coleman 1990). From his perspective,
the following two points characterise the action of placing trust. On the one hand, trust
implies that the truster freely transfers assets to another person, without controlling the
actions of that other person or having the possibility to retaliate. On the other hand,
there must be a potential gain in order to have an incentive to trust. The incentive is such
that the truster is better off than not having trusted if the other person is trustworthy,
and worse off if the other person does not merit the trust placed in him/her. Note
that in this concept, trust is considered a form of behaviour rather than as a personal
characteristic or personal trait.
Our new survey measure is more precise than the GSS question on what dimension
of trust is measured. We focus on trust in strangers. We have used this new survey
measure in the German Socio-Economic Panel (SOEP) study and several independent
studies that are all representative for the German population. In order to distinguish
the newly developed trust questions from the GSS question, we refer to them as ”SOEPtrust”. A factor analysis shows that SOEP-trust (trust in strangers) measures a different
dimension of trust than questions on trust in institutions and trust in known others.
Further, we show that SOEP-trust is a valid and a sensitive measure of trust. Concerning
the latter, we find that SOEP-trust is correlated with social desirability and the position
in the survey. Furthermore, trust is only moderately stable over three weeks. This has
implications for the use of the survey question in international comparisons and over
time.
The design of the simplified trust game is as follows. Two players are each endowed
with 10 euros. The first mover decides how many of his or her 10 euros he or she would
like to transfer to the second mover. Each transfer is doubled by the experimenters.
3
The second mover then gets to know the first mover’s transfer and then decides him or
herself about the back-transfer. As with the first mover, the second mover can transfer
any amount between zero and ten euros. The second mover’s transfer is doubled as
well. Then the game is over and both participants are paid by a cheque. In order to
distinguish the experiment from other trust measures we refer to it as ”EXP-trust” in
the following.
Our implementation of the experiment in a large survey has several advantages over
an ordinary laboratory experiment. With our survey measure of trust, we are able to
directly check whether only highly trusting people decide to participate in the trust
game. Since this is a panel study, we can additionally check whether less trusting people
are more likely to leave the panel in the future. Further, we can compare students and
non-students in the same experimental setting (design and procedure).
Another advantage of the combination of survey and experiments is that we can
use the information in the survey to validate EXP-trust. We thus analyse whether
the decision to trust is influenced by risk preferences, selflessness, and expectations, as
postulated by economic theory. We also assessed the sensitivity of the experimental
design to a social desirability bias, the stake size, and the available strategy space.
We find that EXP-trust is surprisingly robust and also not subject to a social desirability bias, and not dependent on the exact stake size or on the size of the strategy
space. Furthermore, we find that for subjects who are familiar with the interview situation (i.e., through previous participation in a panel study), selection into the experiment
is not subject to their level of trust. In contrast, for subjects who have not previously
been part of a panel study, more trusting people are more likely to participate in the
experiment. And contrary to previous research, we find that students are more trusting than non-students, which has consequences for the generalisability of experimental
results from students to the general population.
Finally we analyse what kind of trust the experiment actually measures. We find
that EXP-trust measures people’s trust in strangers, but not their trust in institutions or
in known others. That is, EXP-trust is significantly correlated with the newly developed
4
SOEP-trust measure. Past trusting behaviour is a good predictor of the behaviour in
the trust game as well.
Thus, on a representative level for Germany, we show that survey and experimental
measures of trust are connected in the way that the trust game measures a specific
dimension of trust, that is, trust in strangers.
In the following, we first analyse the survey measure of trust (SOEP-trust), and in
the second part analyse the experimental trust measure (EXP-trust). In the third part,
we combine these two measures and analyse their similarities and differences.
2
Using Surveys to Measure Trust
In this chapter, we propose a new measure of trust in strangers and analyse its sensitivity,
reliability, and validity. In particular, we show that trust in strangers measures a specific
dimension of trust that is distinct from other dimensions like trust in institutions or trust
in known others. We implement this entire analysis in the framework of the German
Socio-Economic Panel (SOEP). The SOEP is a household panel that contained 22,611
individuals in 12,061 households in the year 2003, comprising a representative sample for
Germany (Wagner et al. 2007). Additionally, we conducted four accompanying studies
(AS), one each year from 2002 to 2006. These accompanying studies all have a randomly
drawn sample of 400 to 1,000 observations of the German population and are thus
representative for Germany as well. Together these data sets provide a great tool to
assess survey questions in a large heterogeneous population. In Table A.1 we list all the
studies used and give an overview of which study we used to implement the different
variations of the study design. In Appendix A.2, we discuss whether our results can be
viewed as representative for trust.
5
2.1
SOEP-trust — A New Measure of Trust
The General Social Survey (GSS) measure of trust, together with the quite similar
question in the World Values Survey (WVS)2 is probably the most widely used question
to measure trust in surveys.
Generally speaking, would you say that most people can be trusted
or that you can’t be too careful in dealing with people?
◦ Most people can be trusted
◦ Can´t be too careful
This question measures people’s expectations of others’ trustworthiness. Based on our
concept of trust, expectations about other people’s trustworthiness is an important factor
in deciding whether one decides to trust or distrust. The advantage of this question is
that the same question is used over time and space, thus allowing a wide array of
different analyses. However, it has been criticized that the respondents have the choice
between trust and caution and not between trust and distrust or between cautious and
incautious behaviour (for a review, see Yamagishi et al. 1999). Although trust and
caution are difficult to disentangle, it is important to measure them separately, since
trust and caution are not necessarily mutually exclusive. The interpretation of the GSS
question can therefore differ widely among different societies (e.g., Gabriel et al. 2002).
Miller & Mitamura (2003) showed, for example, that Japanese students are more trusting
than Americans measured with the above question from the GSS. Measuring trust and
caution separately, they find, however, that American students are more trusting than
Japanese students but at the same time also more cautious. These differing results
clearly demonstrate the problems for the interpretation of the above question.
Based on this evidence, we decided to create a new measure of trust using the German
Socio-Economic Panel. We split the GSS question up into two parts. On the one hand,
we asked people to what extent they agree with the following two statements:
2
Generally speaking, would you say that most people can be trusted or that you need to be very
careful in dealing with people? Most people can be trusted OR Need to be very careful.
6
• In general, you can trust people.
• Nowadays, you can’t rely on anybody.
The possible answers on a four point rating scale were ”disagree strongly”, ”disagree
somewhat”, ”agree somewhat”, or ”agree strongly”.
Another criticism of the GSS question is that answers may differ significantly depending on whether people understand ”most people” in the question as meaning acquaintances or strangers (Reeskens & Hooghe 2008). We therefore let people rate their
agreement with two further statements about trust and caution, in which we clearly
state that trust towards strangers is meant and not towards family or friends:
• How much do you trust strangers you meet for the first time.
• When dealing with strangers, it’s better to be cautious before trusting them.
The possible answers on a four-point rating scale were either ”no trust at all”, ”little
trust”, ”quite a bit of trust”, and ”a lot of trust” for the first question and ”disagree
strongly”, ”disagree somewhat”, ”agree somewhat”, or ”agree strongly” for the second
question. These four statements constitute our new measure of trust in strangers. To
distinguish it from GSS trust, we will call it SOEP-trust in the following.
The emphasis on trust in strangers takes into account that trust is a multidimensional
concept. To test whether SOEP-trust measures trust in strangers specifically, we let
people rate other statements on trust in different institutions like the police or the
government and in acquaintances like friends and family. The list of items can be seen
in Table 1. People could answer on the scale from ”no trust at all”, ”little trust”, ”quite
a bit of trust”, to ”a lot of trust”. Because of the multidimensionality of trust, these
items are expected to measure different aspects of trust. A principal component analysis
over all the trust items can show us how many dimensions these items measure. We find
that these items represent three distinct components.
Table 1 reports the factor loadings of the different items. The bold numbers indicate
the component to which each item belongs. Each component has a straightforward
7
interpretation. The first factor can be interpreted as trust in institutions, the second
factor as trust in strangers, and the third factor as trust in known others. The second
component consists of all the items of SOEP-trust and can thus be interpreted as ”trust
in strangers”. This clearly shows that SOEP-trust measures the specific trust people
have in strangers. It can clearly be distinguished from trust in institutions and known
others.
Table 1: Dimensions of Trust
How much trust do you have in...
... parliament
... public authorities
... the European Union
... courts
... large companies
... churches
... schools and the educational system
... press
... labour unions
... police
... your own family
... neighbours
... friends
... strangers
To what extent do you agree or disagree?
In general, you can trust people.
Nowadays, you can’t rely on anybody.
It’s better to be cautious before trusting strangers.
Factor1
Trust in
institutions
Factor2
Trust in
strangers
Factor3
Trust in
known others
0.742
0.715
0.686
0.665
0.581
0.460
0.564
0.550
0.493
0.584
0.070
0.115
0.045
0.183
0.173
0.139
0.163
0.085
-0.003
0.218
0.088
0.076
0.015
-0.015
-0.051
0.165
0.145
0.636
-0.006
0.107
0.004
0.092
0.033
0.193
0.193
0.081
0.028
0.273
0.647
0.716
0.695
0.091
0.155
-0.106
-0.050
0.647
-0.666
-0.685
0.268
-0.229
0.154
Notes: Factor analysis using the principal components factor method and an orthogonal varimax rotation. The table reports the rotated factor loadings for the three factors with an
eigenvalue larger than 1.
Source: AS02, AS03, AS04, AS05, and AS06 with a total of 3,180 observations.
8
For each of the three dimensions we calculated count indices3 . The reliability of these
scales measured by Cronbach’s alpha is quite good. It is 0.82 for the index on trust in
institutions, 0.62 for trust in known others, and 0.66 for SOEP-trust. Having introduced
these three measures of trust we are interested how sensitive these measures are.
2.2
Sensitivity of SOEP-trust
In this section we assess the sensitivity of SOEP-trust. We check whether the position in
the survey matters, whether there is a social desirability bias, and whether SOEP-trust
is a stable and reliable measure.
2.2.1
Position in the Survey
We varied the position of the items of SOEP-trust in the AS06. Subjects were randomly
divided into two groups. In one group, the trust questions were asked early in the
questionnaire (number 33 out of 118 questions) and in the other group towards the end
(question 93). We compare the ranking and the variance of this variable. The latter
is clearly not dependent on the position in the survey (Levene’s robust test statistic
for the equality of variances: F(1, 1039) = 0.169 P > 0.681). However, the ranking is
affected. Respondents who were asked the questions late in the survey exhibit more trust
in strangers than people who answered the question early in the questionnaire (Table
2). Since the four preceding questions were the same in both situations, this effect is
not likely to be driven by different preceding questions but by the position in the survey.
Although the size of the effect is rather small, this suggests that to compare trust across
time or space, the items have to have a similar position within the survey.
3
The count indices are the mean answer of the non-missing items that load highest on each of the
three dimensions. The bold numbers indicate which item loads highest on which dimension. The value
for a person is calculated as soon as at least two items per component are non-missing. For the count
index SOEP-trust we additionally made sure that at least one caution item and one trust item was
included. For the count index trust in known others, we additionally included an item about trust in
co-workers. This item was not included in the factor analysis, since only people with a job are asked.
An inclusion would thus exclude a major share of the population.
9
Table 2: Position of the trust questions within the survey
To what extent do you agree
or disagree? (percentage agreeing)
- In general, you can trust people.
- Nowadays, you can’t rely on anybody.
- It’s better to be cautious before
trusting strangers.
Count-index: SOEP-trust
Position in
survey
early
late
72.8% 73.4%
36.3% 32.3%
Wilcoxon
rank-sum
test (p)
> 0.298
< 0.073
89.4% 88.2%
early < late
< 0.023
< 0.017
Source: AS06 with a total of 1,033 observations.
2.2.2
Social Desirability
We measure social desirability by the Balanced Inventory of Desirable Responding
(BIDR) developed by Paulhus (1991). Based on this, Winkler et al. (2006) developed a
short version that is suitable for large surveys in the general population. The BIDR has
two dimensions. One is called ”self-deceptive enhancement” and captures a tendency to
see reality in a more optimistic way than justified. This self-deception is not thought
to be conscious. The other dimension is called ”impression management” and measures
the degree to which a person consciously tries to construct a favourable picture of other
people. Since trust is desirable in society, trust questions are found to be correlated with
scales of social desirability (Rotter 1967).
We find that the dimension ”impression management” is significantly positively correlated with the survey measures SOEP-trust (Spearman’s rank correlation: ρ = 0.11),
trust in known others (ρ = 0.15) and trust in institutions (ρ = 0.14). The correlations
of survey trust measures with ”self-deceptive enhancement” are quite low and only significant for SOEP-trust (ρ = 0.07, p < 0.051) and trust in known others (ρ = 0.08, p <
0.036). The correlation is not significant with trust in institutions (ρ = -0.00).
Thus, people who are subject to a social desirability bias are likely to overstate their
trust in strangers, known others, and institutions. This calls into question the validity
of these measures of trust and makes comparisons across space and time difficult.
10
2.2.3
Stability and Reliability
We assessed the stability of SOEP-trust by repeating core questions of the AS05 six weeks
later with a sub-sample (n = 193). If trust measured in a survey is a stable measure, we
expect a correlation close to one. We find that SOEP-trust is only a moderately stable
measure since the Spearman’s rank correlation coefficient between the two time periods
is 0.484 . The trust level did not change for one-third of the participants, increased
for 37%, and decreased for 30%. Since trust is only moderately stable over time, the
reliability of SOEP-trust cannot be assessed through a simple correlation. Instead we
used a composite reliability test according to Raykov (2004), which tests whether a single
factor underlies a certain set of variables. A structural equation model approach is used
to calculate the reliability coefficient ρ. We find that it is 0.81. This shows that our
measure of SOEP-trust is a reliable measure of trust. In sum, though SOEP-trust is
only moderately stable across time, it does prove to be a reliable measure of trust.
2.3
Validity of SOEP-trust
In the previous section we showed that our new survey measure of trust has a high level
of reliability but is sensitive to the position and also has a social desirability bias. In this
section, we assess the validity of the new measure in two different ways. First, we used a
survey measure of past trusting behaviour (Glaeser et al. 2000). Relying on self-reports,
we asked people: ”How often do you...”
• ...lend personal possessions to your friends (CDs, books, your car, bicycle etc.)?
• ...lend money to your friends?
• ...leave your door unlocked?
Respondents’ answers were either ”never”, ”infrequently”, ”sometimes”, ”often”, or
”very often”. A factor analysis confirms the one-dimensionality of these three items.
4
The items of SOEP-trust are correlated as follows: “In general you can trust people” with 0.41,
“Nowadays, you can’t be too careful” with 0.45 and “It’s better to be cautious before trusting strangers”
with 0.34.
11
We interpret this as past trusting behavior trusting . The reliability of these items in a
count index is 0.56. A first indication that the new trust measure is valid stems from
the fact that it correlates significantly with past trusting behavior (Table 3: Spearman’s
ρ = 0.17). Concerning the other two dimensions, we find that trust in known others
correlates significantly with past trusting behaviour (Spearman’s ρ = 0.08) whereas the
correlation with trust in institutions is essentially zero and not significant. The latter is
not surprising since the past trusting behaviour has little to do with trust in institutions.
A second way of assessing the validity of the new trust measure is to compare it to other
established measures of trust. Apart from the formulation in the World Values Survey,
we used two other well-established measures of trust, namely the question from the European Social Survey (ESS) and the original 10 items of the dimension ”agreeableness”
from the NEO-PI-R that measures trust. The ESS-trust question simply asked people
on an 11-point Likert scale how much trust they have in others. As can be seen in Table
3, this ESS-trust correlates highly (ρ = 0.47) with our new measure of trust. A similar
correlation of 0.55 can be observed between the new measure of trust with the trust
factor of the NEO-PI-R and of 0.53 with the GSS question.
From this we conclude that SOEP-trust is a valid measure of trust in strangers, and
is well connected to existing survey measures of trust.
3
Using Experiments to Measure Trust
In this section we assess the sensitivity and validity of EXP-trust in a common framework. Despite its frequent use in economic research, we are not aware of any other study
that analyses the sensitivity and validity of the trust game in depth and at the same
time in the same framework. Furthermore, we have chosen to run the experiment with
a representative sample in order to compare the experiment directly with representative
surveys. Since not all randomly selected persons agree to participate in an experiment,
it is important to check whether results from the experiment can be generalised to a
whole population. That is why we start by analysing a possible selection effect, then
12
Table 3: Correlations between different concepts of trust in surveys: ESS, NEO-A1, and
GSS
SOEP-trust
- In general you can trust people.
- Nowadays, you can’t rely on anybody.
- It’s better to be cautious before
trusting strangers.
- Trust in first-time met stranger
Trust in known others
Trust in Institutions
self-report.
trusting
behaviour
0.17***
0.10***
-0.15***
ESS
NEO-A1
GSS
0.47***
0.37***
-0.34***
0.55***
0.49***
-0.40***
0.53***
0.47***
-0.39***
-0.13***
0.08***
0.08***
0.02
-0.19***
0.34***
0.29***
0.28***
-0.31***
0.33***
0.31***
0.31***
-0.48***
0.22**
0.34***
0.29***
Notes: Spearman’s rank correlations. The table reports the correlation coefficients and the significance
level is denoted as follows:
∗ : 10%
∗∗ : 5%
∗ ∗ ∗ : 1%
Source: AS02, AS03, AS04, AS05, and AS06 and the data on the GSS trust question was collected in a
separate student survey at Royal Holloway, University of London in June 2008.
analyse its sensitivity and validity.
3.1
Design of the Experiment
The design of the experiment to measure trust is based on the investment game introduced by Berg et al. (1995). Two players anonymously interact with each other in the
following way. The first mover gets an endowment of 10 points and can transfer zero
to ten points to the second mover. Every point that is transferred is doubled by the
experimenters. The second mover also gets an endowment of ten points. After receiving
points from the first mover, he/she decides on how much of the endowment to transfer
back to the first mover (zero to ten points). As with the first mover’s transfer, the backtransfer by the second mover is doubled by the experimenters. After the second mover’s
decision, the game ends and the subjects are paid their income in euro (one point equals
one euro) by cheque sent a few days later.
This design was developed by Fehr, Fischbacher, Schupp, von Rosenbladt & Wagner (2002) and it was implemented in the SOEP 2003 and in the AS02. In the other
two accompanying studies, we used a small variation of the design. In the AS03, we
13
implemented the strategy method for the second mover. In order not to make it too
complicated, we restricted the options of both players to transfer either zero, five, or ten
points to the other player. In the AS04, we removed the option to transfer half of the
endowment to the other player by allowing them to transfer only zero, two, four, six,
eight, or ten points to the other player. In the SOEP 2003 we implemented a high-stakes
treatment that used an exchange rate of one point to ten euros for a small part of the
sample (117 out of 1,432). Thus, both players had an endowment of one hundred euros.
The Nash equilibrium of this trust game can be described as follows. If we assume
that both players are rational and selfish and that this is common knowledge, neither
one of the players ever transfers a single point to the other. We can relax the assumption that both players are selfish and instead assume that both players are inequalityaverse as described by Fehr & Schmidt (1999). If the second mover is inequality-averse
enough(β > 13 ), his/her back-transfer is equal to the first mover’s transfer. If the second mover’s inequality-aversion is not common knowledge, the first mover’s transfer
depends on his/her belief about the probability that the second mover will equalise
the inequality. If this belief is above a certain threshold, the first mover transfers the
whole endowment and otherwise nothing at all. One can show that the more inequalityaverse the first mover, the higher this threshold is. Another relaxing assumption is
that players have a preference for reciprocity instead for complete selfishness (Falk &
Fischbacher 2006). With this assumption, the second mover’s back-transfer increases
weakly with the strength of his/her preference for reciprocity. Note that the prediction is not only a back-transfer of zero or equal to the first mover’s transfer, but that
transfers in between are possible as well. However, the first mover’s transfer is predicted
to be either zero points or the whole endowment. He/she will transfer zero points if
his/her second-order beliefs are low enough, that is, below a certain threshold. Otherwise, first movers will transfer the whole endowment. This threshold increases with the
first mover’s degree of reciprocity. Thus, the more reciprocal a first mover is, the less
he/she is predicted to transfer in a trust game.
In sum, a preference for equity or reciprocity increases the likelihood that the second
14
mover is behaving in a trustworthy manner. However, it decreases the probability that
the first mover trusts given the second mover’s preferences.
3.2
Selection
Imagine the situation that someone knocks on your door and asks you to participate in
a survey on politics and society that would last about an hour. Included in the survey
is a ”game” in which you can earn some money. Many people will be suspicious and
mistrust the person at the door. This situation is common for the interviewers in the
social research section of TNS Infratest in Munich. They conduct the interviews for
SOEP’s accompanying studies as well as for the panel study itself. Besides other factors,
the distrust in the interviewer and/or the survey organisation may lead people to refuse
to participate in the survey.
If one wants to study trust on a representative level, it is therefore crucial to avoid a
randomisation bias (Heckman & Smith 1995) due to trust. The issue has grown in importance in light of recent criticism of experimental economics (Levitt & List 2007), which
is confronted with the same selection problem. So far, however, there has been little
discussion about selection into either lab or field experiments. In one of the few studies
that has addressed the problem, Bellemare & Kroeger (2007) do not find any randomisation bias for a trust experiment in a random sample of 541 regular panel participants
in the Netherlands. Contrary, Harrison et al. (forthcoming) find a randomisation bias
in a sample of 253 Danish subjects who were not part of a panel study but recruited for
a ”snapshot study” like our accompanying studies. They found that risk-averse people
were less likely to participate in their study. These two studies indirectly assess the
randomisation bias for the variable of interest.
The aim of this section is to examine the two possible randomisation biases in our
study design. The first is that less trusting persons will be less likely to participate in
the survey and second, that conditional on participation in the survey, it is less likely
that they will agree to participate in the experiment at the end of the questionnaire. It
is important to analyse these effects separately, since survey respondents know what the
15
survey is about, while participants in an experiment normally do not see its ultimate
purpose.
The participation bias in the survey can be addressed by weighting subjects such
that the distribution of basic variables conforms to the German population (Kroh &
Spieß 2008). We can then compare the level of trust with and without weights. Different
levels would then be an indication that selection is an issue. Indeed we do not find any
indication that the weighted mean is significantly different from the unweighted one in
the panel or in the accompanying studies. The average weighted transfer (mean: 5.15;
n = 1454) lies in the 95% confidence interval of the unweighted mean (5.02 - 5.31). This
holds also for the panel and the accompanying studies separately.
The second randomisation bias, refusal to participate in the experiment, can be addressed by comparing trust measured in the survey for participants in the experiment
with non-participants. Since all the subjects who refused to participate in the experiment
completed the survey, we are able to directly assess their trust through trust measured
in the survey. As a proxy for trust in the interviewer and the survey organisation, we
take SOEP-trust and past trusting behaviour. The latter captures the experience of past
interactions that involved trusting others. We find that in the SOEP, past trusting behaviour nor SOEP-trust are related to the refusal to participate in the experiment (Mann
Whitney test: past trusting behaviour z=0.673 p>0.500; SOEP-trust 0.505 p>0.613).
Contrary to the SOEP, we find in the accompanying studies a significant lower level of
trust among people who refused to participate in the experiment than those who participated (Mann Whitney test: past trusting behaviour z=2.23 p<0.026; SOEP-trust 1.322
p>0.185).
One likely explanation for a randomisation bias due to trust in the AS but not in
the SOEP is based on the different set-ups. In the SOEP, subjects are familiar with
the survey organisation and usually also with the interviewer, since these persons have
been participants in the panel for three years. In accompanying studies, on the other
hand, people are coming into contact with the survey organisation and the interviewer
for the first time. In the accompanying studies, we find that in 2003, 5.1% and in 2004,
16
10.8% of subjects refused to participate in the experiment. In the panel study SOEP,
only 4.8% of 1,504 subjects who completed the questionnaire refused to participate in
the experiment in 2003. The difference in refusal rates between AS and SOEP is highly
significant (Fisher’s exact test: p<0.001).
This interpretation is supported by the following two facts. First, the null effect in
the SOEP cannot be explained by a ceiling effect, since past trusting behaviour is even
slightly higher in the accompanying studies than in the SOEP 2003. Second, we find
that people who refuse to participate in the experiment are also more likely to leave the
panel in the following three years (Fisher’s exact test: p<0.001). However, the people
who left the panel did not exhibit different trust levels in the experiment (Mann-Whitney
test: z=0.387 p>0.698) or in the survey (Mann-Whitney test: past trusting behaviour
z=0.849 p>0.395; SOEP-trust z=1.529 p>0.126).
In the accompanying studies, people were asked about their willingness to participate
in a similar study another time. Similar to the panel study, we find that people who
refuse to participate in the experiment are also less willing to participate another time
in a similar study (Mann-Whitney test: z=10.641 p<0.000). Unlike in the panel study,
we find that people who are less willing to participate in another similar study exhibit
less trust in the experiment (Spearman’s rank correlation ρ = 0.08 p<0.034) as well as
in the survey (Spearman’s rank correlation: past trusting behaviour ρ = 0.13 p<0.001;
SOEP-trust ρ = 0.02 p>0.504).
Concerning risk-aversion we find very similar results as those for trust and found in
Harrison et al. (forthcoming). We measured people’s risk aversion by asking ”Are you
generally a person who is fully prepared to take risks, or do you try to avoid taking
risks?”. People answered on a Likert scale. This measure of risk-aversion is shown to
be a valid measure by comparison to a lottery experiment with real monetary stakes
(Dohmen et al. 2007). As with trust, we find that in the panel study SOEP, 5 there is no
significant relation between risk preferences and the participation rate (Mann-Whitney
5
The question on risk aversion was asked one year after the experiment in 2004. We assume that risk
preferences are stable over this time period
17
test: z=1.487 p>0.136), whereas in the AS03 the most risk-averse people are less likely
to participate in the experiment than the least risk-averse (Mann-Whitney test: z=2.135
p<0.033).
Thus, our results suggest that representative trust games are subject to selection
effects due to trust and risk preferences if subjects are unfamiliar with the situation they
are in (e.g., unknown survey organisation and/or interviewer).
In Appendix A.3, we additionally analyse other factors that might influence the participation in the experiment, including social preferences, personality, and demographic
variables. In sum we find that selflessness, reciprocity, and interviewer characteristics do
not matter in the decision to participate in the experiment. A medium income, a large
household size, and living in Eastern Germany reduce the probability that a person will
refuse to participate. Finally, the longer the previous questionnaire lasted, the more
likely it is that participants will refuse to participate in the present experiment in the
AS.
In sum, we found that the level of trust is related to the decision to participate
in the experiment for subjects who are participating for the first time in this kind of
interview. On the other hand, trust has no influence if subjects are familiar with the
general set-up of the study. Based on these findings we conclude that a longitudinal panel
survey where a ”trust relationship” between the survey institute and the respondents
is already established is the best way to minimise the total response error when adding
experimental add-ons.
3.3
Validity of experimental measure
The decision to trust is influenced by people’s preferences and expectations. Risk and
social preferences and expectations about the other player’s behaviour are expected to
shape the decision to trust (Coleman 1990). Thus, one can check the validity of the
trust measure by showing that these preferences and expectations indeed correlate with
EXP-trust.
We measured people’s risk-aversion as outlined in section 3.2. We find that the
18
first mover transfer is higher the less risk-averse people are in AS03 (Spearman’s rank
correlation ρ = 0.13 p<0.012).6 Thus, people’s risk preferences influence their decisions
to trust or mistrust.
Selflessness is one component of social preferences, and we measured selflessness by
asking people how often they volunteer in clubs and social services. They answered by
indicating whether they volunteered ”never”, ”seldom”, ”at least once a month”, ”at
least once a week”, or ”daily”. This question is asked in every AS and in the SOEP. We
find that the more often people volunteer — thus behaving more selflessly — the more
they transfer to the second mover (Spearman’s rank correlation ρ = 0.06 p<0.016).
In the AS03 we elicited first movers’ expectations by asking how much they expected
the second mover to transfer back if they were to transfer zero, five, or ten points to the
second mover. A selfish first mover would like to maximise his/her payoff. We therefore
calculated which of the three transfers (zero, five, or ten) a first mover expected to
maximise his/her payoff.7 The higher the transfer needed in order maximise expected
profits, the more a selfish first mover is expected to transfer. We find a positive and
significant correlation (Spearman’s rank correlation ρ = 0.20 p<0.003). However, a
non-selfish first mover is not primarily interested in making the transfer that he/she
expects to maximise profits. If he/she cares about inequality, he/she would like to know
which of the three transfers yields the lowest inequality.8 With our data, this is also
possible to calculate, and the higher the transfer expected to minimise inequality is, the
higher we expect the first movers’ transfers to be. This other measure for expectations
is positively correlated with first mover’s transfer, as well (Spearman’s rank correlation
ρ = 0.31 p<0.001). As a third measure, we calculate the average expected back-transfer,
which can be interpreted as a general measure for the expectation of the second mover’s
selflessness. Again, we find a positive correlation (Spearman’s rank correlation is: ρ =
0.18 p<0.005).
6
In the SOEP the survey measure of risk was implemented in 2004, that is, one year after the
experiment took place. We still find a significant relation between first-mover transfers and the risk
measure in the survey (Spearman’s rank correlation ρ = 0.08 p<0.048).
7
Where the profit was the same for two or three transfer levels, we chose the lowest of them.
8
In case the inequality is the same for two or three transfer levels, we chose the higher of them.
19
In sum, we have shown that EXP-trust is influenced by risk and social preferences
and by expectations about the second mover’s behaviour. Further, in Section 4, we
demonstrate that EXP-trust is correlated with survey measures of trust. All these
results are strong indications that the EXP-trust is a valid measure of trust.
3.4
3.4.1
Sensitivity of experimental measure
Students versus general population
A common critique of laboratory experiments is that students, the preferred subject
pool in experiments, may behave systematically differently than non-students (Levitt &
List 2007). In addition, there are a number of studies showing that students behave differently than other groups or that economics students are different from non-economics
students (e.g., Fehr et al. 2006). A stronger test of the claim that students behave systematically differently is to compare their behaviour to a representative sample of the
general population. Only a few studies have been designed this way, and the following
results are found. In the U.S. state of Vermont, Carpenter et al. (2007) found in a field
experiment that students donated 17 dollars less to charities than non-students, who
donated 72 dollars out of 100. In a representative ultimatum game in Taiwan, no difference was found between students and non-students (Fu et al. 2007). Concerning discount
rates, Harrison et al. (2002) found that in Denmark, students have a six percentage point
higher discount rate than non-students. In a similar study in Denmark, students were
found to be more risk-averse than non-students (Harrison et al. 2007). Since students
are found to be more risk-averse and less pro-social, previous studies have suggested that
students transfer fewer points in trust games than non-students. Indeed, this result was
found by Bellemare & Kroeger (2007) for the Netherlands, where students transferred
much less in a trust experiment than a representative population sample.
Among our German sample of 1,665 first movers in the trust game, we identify 47
as students. We find the opposite of all previous studies in that students transfer 61%
whereas non-students transfer 50% of their endowment. Thus, students exhibit a 21%
higher level of trust than non-students. This is difference is highly significant (regres20
Table 4: Dependent variable: Level of trust (first
(1)
(2)
Dummy for being a student
0.967**
0.992**
(0.440)
(0.434)
Dummy for having a university
0.592***
degree
(0.193)
Age
Age2
Household income: <2500 Euro
(Base: <1500 Euro)
Household income: <3500 Euro
Household income: <5000 Euro
Household income: >5000 Euro
Risk aversion: medium
(Base: high)
Risk aversion: low
Constant
5.081***
(0.072)
1539
0.00
N
Adjusted R2
mover transfer)
(3)
(4)
0.773*
1.219**
(0.449)
(0.570)
0.464**
(0.203)
-0.037
(0.023)
0.000
(0.000)
-0.083
(0.196)
-0.105
(0.218)
0.404
(0.248)
1.029***
(0.321)
0.309*
(0.181)
0.655***
(0.246)
4.986***
5.855***
4.881***
(0.078)
(0.528)
(0.136)
1539
1539
1056
0.01
0.02
0.01
Notes: Models 1 - 4 are estimated using an OLS specification. Numbers in parentheses denote the
robust standard errors. The number of observations is lower in model four since data on the survey
question on risk aversion is only available for people in the SOEP and AS03. Significance levels are
denoted :
∗ : 10%
∗∗ : 5%
∗ ∗ ∗ : 1% Source: SOEP 2003 and AS02, AS03, and AS04
21
sion 1 in Table 4). The results from the survey measure of trust strongly support these
findings. SOEP-trust, trust in institutions, and past trusting behaviour are more pronounced among students (Mann-Whitney test: SOEP-trust z = 10.521 p<0.001; trust
in institutions z = 2.88 p<0.005; trust in known others z = 0.01 p>0.99; past trusting
behaviour z = 19.78 p<0.001). Students are typically younger than the average population, have a lower income, and a higher level of education. Do these different observable
characteristics explain the observed differences? Controlling for these characteristics, we
find that students do not trust more because they have a higher education, although
people with a university degree have a higher level of trust than those without. The
difference between students and non-students is still highly significant and similar in
magnitude to findings when not controlling for university degree (regression 2 in Table
4). Further, we controlled for age and income and found that these variables decreased
the coefficient for the dummy for students by about 22% but remained weakly significantly different from zero (regression 3 in Table 4). Thus, in the German population
with our survey measure of risk aversion, we find that students seem to be less risk-averse
than non-students. Therefore it is important to control for whether lower risk aversion
can explain the higher level of trust among students than non-students. As expected,
risk aversion is a determinant of trusting behaviour but the differences between students
and non-students remain highly significant (regression 4 in Table 4). Trustworthiness
does not differ between students and non-students (regression 1 in Table 5). Thus, students exhibit a higher level of trust than non-students. Differences in age and income
reduce the difference by 22% but different degrees of risk aversion cannot explain the
fact that students are more trusting than non-students.
3.4.2
Stakes
Only a few studies have examined the effects of stake size on behaviour in experiments.
Most of these studies have analysed the ultimatum game and found that first movers’
offers are independent of the stake size9 . However, it is also found that the respondents’
9
An exception was reported by Fu et al. (2007), who found that offers decrease with stakes.
22
minimal acceptable offer is lower with high stakes than with low stakes (Hoffman et al.
1996, Slonim & Roth 1998, Cameron 1999, Munier & Zaharia 2002, Fu et al. 2007). No
significant effects are found in the dictator game (Forsythe et al. 1994, Cherry et al.
2002, Carpenter et al. 2005, List & Cherry 2008) or in the gift-exchange game (Fehr,
Fischbacher & Tougareva 2002). Mutual trust measured in the centipede game is reduced
significantly with lower stakes (Parco et al. 2002).
Johansson-Stenman et al. (2005a) are the only ones that have studied the effect of
stake size in the trust game. In contrast to the findings in the gift-exchange game and the
ultimatum game and in line with the centipede game, they found that first movers’ transfers in rural Bangladesh were lower the higher the stakes. The proportions transferred
to the second movers was 55% in the case with low stakes. The proportion transferred
decreased to 46% in the medium-stakes condition, and to 38% in the high-stakes condition. The stakes were equivalent to 67, 337 and 1683 U.S. dollars. Concerning the
behaviour of the second-mover, no difference was found for the different stake sizes.
Figure 1: Levels of Trust: Different stake sizes
Endowment of 10 Euro (SOEP 2003)
Endowment of 100 Euro (SOEP 2003)
.6
relative frequencies
.5
.4
.3
.2
.1
0
0
1
2
3
4
5
6
7
8
9 10
0
1
2
3
4
5
6
7
8
9 10
Levels of trust (0=lowest 10=highest) (first mover transfer)
We compared the differences between endowments of 10 and 100 euros. Contrary to
23
the results of Johansson-Stenman et al. (2005a), we find no differences in the average
level of trust (Figure 1: t-test: t = 0.51 p>0.607). The high-stakes endowment was ten
times higher than the low-stakes one, and so was the average transfer (5.16 versus 49.9
euros). The distributions also do not differ (Kolmogorov-Smirnov test: p>0.968). The
levels of trustworthiness are not different in the two treatments either (regression 2 in
Table 5). Our findings are based on stakes that are rather low compared to the average
income in Germany rather low, but cover the size of many everyday trust relations.
3.4.3
Social desirability
Contrary to the questionnaire, the decision in the experiment remains private and is not
communicated to the interviewer. Thus social desirability is not expected to influence
the decision. We again use the Balanced Inventory of Desirable Responding by Paulhus
(1991). We indeed find that impression management (Spearman’s rank correlation ρ =
0.08 p>0.145) and self-deception (Spearman’s rank correlation ρ = 0.03 p>0.590) are
not significantly correlated with EXP-trust. The decision to behave trustworthily or not
is not correlated with social desirability either (regression 3 in Table 5).
24
Table 5: Sensitivity of experimental measure of trustworthiness
Dependant variable:
dummy of being trustworthy
Dummy for being a student
Students
Stakes
Social desirability
-0.001
(0.048)
Dummy for 100 Euro treatment
-0.072
(0.052)
Soc. desir.: Impression (std.)
Soc. desir.: self-deception (std.)
Controlled for first mover transfer
Constant
YES
0.535***
(0.020)
1912
0.131
YES
N
Adjusted R2
Cluster on individual level
Notes: OLS regression with robust standard errors :
Source: AS02, AS03, AS04 and SOEP 2003
25
YES
0.518***
(0.047)
602
0.166
NO
∗ : 10%
-0.020
(0.028)
-0.001
(0.028)
YES
0.606***
(0.058)
292
0.014
NO
∗∗ : 5%
∗ ∗ ∗ : 1%
4
Comparing Experimental and Survey Measures of Trust
It yet remains uncertain if our experimental measure captures the same kind of trust as is
described by the various survey measures of trust. Previous research yielded ambiguous
answers when comparing EXP-trust to GSS-trust. Given the criticisms lodged against
this measure, this may not come as a surprise. In GSS-trust, it is unclear, for example,
what or who it is that we trust. In Section 2.3 we showed that GSS-trust is correlated
with several dimensions of trust such as trust in strangers, trust in known others, and
trust in institutions. Unclear relations between GSS-trust and the experimental measure
of trust are probably due to weakness of the survey question rather than problems with
the experiment. Further indications for this can be found in Glaeser et al. (2000) and
Gachter et al. (2004). Both studies report that their experimental measure of trust is
not correlated with GSS-trust.
However, Glaeser et al. (2000) and Gachter et al. (2004) find that their experimental
measures of trust are clearly correlated with survey questions on past trusting behaviour
and a question on trust in strangers, which is formulated as the statement ”You can’t
count on strangers anymore” and is similar to statements on which SOEP-trust is built.
Thus, the question should not be whether survey measures of trust are correlated with
experimental measures or not, but rather what kind of trust the trust game measures.
To this effect, we measured trust in strangers (SOEP-trust), trust in known others, trust
in institutions, and past trusting behaviour.
With our surveys, we have all the ingredients needed to test what kind of trust the
trust game (EXP-trust) actually measures. We find that EXP-trust is significantly correlated (ρ = 0.12) with SOEP-trust (Table 6). Not only the overall measure (SOEP-trust)
but also all its components are correlated with the experimental measure. Furthermore,
EXP-trust is not correlated with the index ”trust in institutions” nor the index ”trust in
known others”. The fact that none of the single items of these two indexes are correlated
significantly with EXP-trust further confirms that the experimental measure specifically
measures trust in strangers.
26
Table 6: Correlations of different survey measures of trust with EXP-trust
Different indices of survey measures of trust
SOEP-trust
Trust in institutions
Trust in known others
past trusting behaviour
To what extent do you agree or disagree?
In general you can trust people.
Nowadays, you can’t rely on anybody.
It’s better to be cautious before trusting strangers.
How much trust do you have in...
strangers
your own family
neighbours
friends
co-worker
churches
schools and the educational system
press
labour unions
police
parliament
public authorities
the European Union
courts
large companies
How often do you ...
lend personal possessions to friends?
lend money to your friends?
leave your door unlocked?
Do you think most people would try to take
advantage of you if they got a chance,
or would they try to be fair? (GSS-fair)
Would you say that most of the time people
try to be helpful, or that they are mostly
just looking out for themselves (GSS-help)
Source: SOEP 2003 and AS02, AS03, and AS04
27
Spearman’s
ρ
sign.-level
obs.
0.116
0.022
0.013
0.156
0.000
0.493
0.682
0.000
1661
952
949
1654
0.066
-0.107
-0.099
0.007
0.000
0.000
1660
1702
1704
0.137
-0.008
0.010
0.038
0.041
0.001
0.054
-0.014
-0.013
0.027
0.034
0.025
0.018
0.037
-0.034
0.000
0.814
0.768
0.239
0.257
0.965
0.102
0.669
0.688
0.411
0.292
0.439
0.591
0.259
0.301
732
946
947
946
764
940
930
945
923
948
943
945
923
941
931
0.140
0.097
0.107
0.000
0.000
0.000
1653
1653
1692
0.067
0.006
1679
-0.004
0.880
1683
Furthermore, we find that self-reported past trusting behaviour is significantly correlated with the experimental measure of trust (ρ = 0.16) and that all the three items
of the index ’past trusting behaviour’, – lending money, lending possessions, and leaving
the door unlocked – are significantly related to experimental trust.
We additionally analysed two frequently used survey questions on fairness and helpfulness that are implemented in the GSS as well. The questions are ”Do you think most
people would try to take advantage of you if they got a chance, or would they try to be
fair? (GSS-fair) and ”Would you say that most of the time people try to be helpful, or
that they are mostly just looking out for themselves” (GSS-help). We find that trusting
behaviour is significantly positively correlated with fairness measured by GSS-fair but
that the correlation is rather low (ρ = 0.07). GSS-help, on the other hand, is not related
to trusting behaviour.
We have thus shown that trust measured in the experiment actually measures a
specific kind of trust, namely trust in strangers. Whereas this in itself is perhaps not
surprising, given the nature of the experiment where participants are in fact unknown
strangers to each other, it is noteworthy on the other hand that EXP-trust has almost
nothing to do with trust in institutions nor with trust in known others.
The correlations of trust measured in the experiment and SOEP-trust (ρ = 0.12)
and past trusting behaviour (ρ = 0.16) are rather low, although they are significantly
different from zero. The reason for this could be that the survey measure SOEP-trust
mainly measures expectations of other people’s trustworthiness. The statements ”In
general, you can trust people” and ”Nowadays, you can’t rely on anybody” give strong
indications of how a person sees strangers. In the experiment, however, the decision to
trust or not is not only influenced by expectations of others’ trustworthiness but also
by risk and social preferences (Naef et al. 2009). If the measure SOEP-trust in fact
mainly measures the expectation part of the motivation to trust, then we would expect
the correlations between the survey measure and expectations in the experiment to be
rather high – in particular, higher than the correlations with the actual decision (for a
related argument see also Sapienza et al. (2007)).
28
Table 7: Correlations between Expectations and behaviour in the experiment
SOEP-trust
Trust in known others
Trust in institutions
past trusting behaviour
(number of observations)
transfer expected
to be ...
inequality
profit
minimising maximising
0.19***
0.17***
0.09
0.08
0.13**
0.12*
0.17***
0.15**
(256)
(246)
average
expected
backtransfer
0.23***
0.11*
0.15**
0.18***
(246)
firstmover
transfer
0.12***
0.01
0.02
0.16***
(var.)
Notes: Spearman’s rank correlations. The table reports the correlation coefficients and the significance
level is denoted as follows:
∗ : 10%
∗∗ : 5%
∗ ∗ ∗ : 1%
Source: AS03
We measured first movers’ expectations of the behaviour of the second movers as
outlined in Section 3.3. We calculate three measures that are: ”the transfer expected
to be profit-maximising”, ”the transfer expected to be inequality-minimising”, and ”the
mean expected back-transfer”. We indeed find that survey measures of trust correlate
more strongly with expectations than with first mover transfers (Table 7). Interestingly,
expectations correlate not only with SOEP-trust but also with our measure for trust
in institutions as well. Thus, expectations seem to be less specific to one dimension of
trust than is the first mover decision in the trust game. Whereas expectations measure
trust in institutions and SOEP-trust, the decision in the experiment is only related to
the survey measure for SOEP-trust.
Trust is a widely researched concept and many different factors have been found to
be correlated with trust. It would be interesting to see if these factors are correlated
with the experimental measure of trust similarly – or differently – than how they are
correlated with survey measures of trust. As a further illumination of the relation of
survey and experimental measures of trust we investigate exactly this question. In order
to answer this question, we chose prominent factors that previous research has shown
to be correlated with trust. As measures of trust, we compare trusting behaviour in the
experiment, SOEP-trust, and the different measures of expectations in the experiment
as explained above. Several studies have reported that socio-economic variables such as
29
Table 8: Different Trust Measures and recently found Correlations of Trust
Age
Being female
Education
Household Income
Being foreigner
Living in East Germany
Religious
Being undenominational
Risk aversion
Negative reciprocity
Freq. of Volunteering
Being an Entrepreneur
Being a shareholder
Appr. number of obs.
Experimental
trust
(same
-0.04*
-0.00
0.09***
0.08***
-0.05*
-0.06**
0.00
0.01
-0.13***
-0.06*
0.06**
0.06*
0.07*
1,660
SOEP
trust
obs.)
-0.01
0.01
0.11***
0.14***
-0.02
-0.11***
0.12***
-0.08***
-0.13***
-0.08*
0.12***
0.04***
0.05
1,660
SOEP
trust
(all obs.)
-0.01*
-0.02***
0.16***
0.15***
-0.04***
-0.09***
0.11***
-0.06***
-0.14***
-0.10***
0.13***
0.04***
0.12***
25,500
transfer expected
to be ...
inequal.
profit
minim. maxim.
-0.15*** -0.13***
-0.06*
-0.05
0.15***
0.12***
0.11***
0.12***
-0.01
-0.03
0.02
-0.03
0.07*
0.05
0.06
0.02
-0.03
0.04
0.02
0.02
0.05
0.02
0.04
-0.02
0.12**
0.10**
800
800
av.
exp.
backtransfer
-0.08**
-0.05
0.10***
0.06
-0.03
0.01
0.11***
0.05
0.09
-0.03
0.06*
0.02
0.05
800
Notes: Spearman’s rank correlations. The table reports the correlation coefficients and the significance
level is denoted as follows:
∗ : 10%
∗∗ : 5%
∗ ∗ ∗ : 1%
Source: SOEP 2003, SOEP 2005, AS02, AS03, AS04, and AS05
age, gender, income, education, nationality, and place of living are correlated with trust
(e.g., Alesina & La Ferrara 2002, Bellemare & Kroeger 2007, Rainer & Siedler forthcoming, Sutter & Kocher 2007). We find that the behavioural trust measure and SOEP-trust
are correlated in similar ways with socio-economic variables10 (Table 8). The only exception is gender where we find lower trust in women than in men when measured by
survey, but no gender difference in trust when measured by experiment. Concerning religion, our results for Germany differ from previous studies in other countries (e.g., Guiso
et al. 2003). We find that people with no religious affiliation exhibit lower SOEP-trust
and people who are actively religious have higher SOEP-trust. Both effects cannot be
confirmed with the behavioural measure of trust. Risk and social preferences (volunteering and negative reciprocity) are correlated significantly with both the survey and
the experimental measure of trust with similar magnitudes. Finally, trust is found to be
10
We do not find that the relation of trust and age is quadratic
30
higher among entrepreneurs and shareholders (Guiso et al. 2006, Guiso et al. 2008). In
our data, we find that both EXP-trust and SOEP-trust are higher for entrepreneurs and
shareholders. Concerning expectations, we find that they are related to age, education,
income, religiosity, and being a shareholder, but not or only marginally to the other
factors. In sum, we find that trust measured by the experiment has similar correlations
with factors that have been reported, as trust measured by survey. This is another
indication that both - EXP-trust as well as SOEP-trust - are valid measures of trust.
5
Conclusion
We have developed a compact survey measure of trust in strangers (SOEP-trust) that
takes into account recent criticism of the widely used GSS/WVS question. We rigourously
tested this measure and find that it is a valid and reliable measure of trust in strangers.
However, one has to be careful in using SOEP-trust since it is a sensitive measure. This
has implications for the use of the survey question in international comparisons and over
time. This does not necessarily devalue our measure as compared to other survey trust
measures, as these most likely exhibit the same sensitivities; however, we did not explore
these.
We analysed an experimental measure of trust extensively and most importantly,
always in the same setting. We showed that there may be a selection of more trusting
people into the experiment if the individuals are participating in such a survey for
the first time, whereas in the panel study, we do not find that selection is an issue.
The experiment is quite insensitive to various changes. We find that stakes, social
desirability, strategy space, and use of the strategy method do not affect the behaviour
in the experiment in significant ways. However, we find that students, who are typical
subjects of lab experiments, behave differently than non-students in that they trust
strangers more than non-students. This finding is confirmed by the survey, where we
find that SOEP-trust is higher among students than among non-students. Furthermore,
we show that trusting behaviour is influenced by people’s risk and social preferences as
31
well as their expectations.
In combination, we find that the experimental measure of trust is significantly correlated with SOEP-trust, which is specifically aimed to measure trust in strangers; but
not with an index of trust in institutions and an index of trust in known others. Furthermore, experimental trust correlates with related factors similarly as SOEP-trust does.
We conclude that the common experimental measure of trust is a valid measure, which
captures a specific form of trust: trust in strangers.
32
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36
A
Appendix
A.1
Overview of the different studies
Table A.1: Overview of the different studies
Study
AS02
SOEP 03
AS03
AS04
AS05
AS06
A.2
No. of obs.
Survey Exp.
442
442
22’611 1’432
846
803
772
689
1’012
1’063
stake
size
10
10/100
10
10
scale
11 options
11 options
3 options
6 options
strategy
method
no
no
yes
no
survey
sensitivity
NEO-FFI
reliability
question order
ESS question
Selection in the survey
Did only highly trusting people participate in the survey? If this were the case, we could
not claim that our results are representative for Germany. We address this potential
problem in three different ways. First we compare the level of trust when people are
weighted such that the distribution in some basic characteristics conforms with the
population distribution and when people are not weighted. Second, we check whether
it makes a difference whether people are familiar with the survey organisation and/or
the interviewer or whether the situation is unfamiliar by comparing the AS03 with the
SOEP in 2003. Finally we know who has left the panel since 2003 and we can check
whether this decision is dependent on their trust level in 2003.
In the accompanying studies, the unweighted mean of SOEP-trust (2.38) lies in the
95% confidence interval of the weighted mean (2.35 - 2.39). Similarly, no differences
between the weighted and unweighted means are found for ”trust in known others” or
for ”trust in institutions”. In the SOEP, the unweighted mean of SOEP-trust (2.308)
lies inside the 95% confidence interval of its weighted mean (2.29 - 2.31) as well. Further
the weighted means in the AS03 (2.30) and in the SOEP 2003 (2.30) are not significantly
different from each other (t-test: t = 0.03, p >0.974). That is, it makes no differences for
37
SOEP-trust whether it is the first time people are interviewed, as in the accompanying
study, or whether people have been in a panel for at least four years. The final test for
a randomisation bias is that we know who left the panel between 2004 and 2006 and we
know what their level of trust was in 2003. There is no difference in the mean of SOEPtrust between those who left the panel and those who stayed the following two years
(t-test = 1.24 P > 0.21). In the accompanying studies we do not have this measure, but
people were asked whether they would like to participate in similar study again. Again
this can be used as an indication whether there is a randomisation bias due to trust.
Again, we do not find any significant difference (t-test = 1.34 P > 0.17).
In sum we find that the participation in the survey in general is not influenced by
how much trust people have.
A.3
Selection in the experiment
Beside trust we are able to test whether social preferences, personality characteristics,
demographic variables, interviewer characteristics and the length of the questionnaire
are determinants of refusal to participate in the experiment (Table A.2). As a proxy for
social preferences we take the frequency with which subjects volunteer and participate
in politics and citizens’ initiatives. Again, we find that in the AS and in the SOEP 2003
there is no significant impact on the rate of refusal. Concerning the personality measures
we find that positive and negative reciprocity do not predict refusal in the experiment
either. However, some demographic variables do explain refusal to participate. There is
a slight tendency for married people to be less likely to participate in the experiment.
Further we find that in the SOEP 2003, people with a high or low household income
are more likely to refuse to participate. In the AS, people in larger households are more
cooperative in participating, whereas East Germans are less likely to participate. A
further test is the length of the survey as a proxy for an additional response burden.
The interviews in the AS04 were conducted using a laptop that recorded the time used
for each question. We thus test whether the length of time from the beginning of the
survey to the decision to participate in the experiment predicts this decision. We indeed
Table A.2: Dep = Dummy of whether a person refused to participate in the experiment
SOEP03
AS03
AS04
Dummy of volunteering at least sometimes
-0.008
0.008
0.044
(0.013)
(0.014)
(0.028)
Dummy of participating in political parties,
-0.005
0.039
-0.010
citizens’ initiative
(0.017)
(0.029)
(0.027)
Negative reciprocity
0.009
(0.007)
Positive reciprocity
0.015
(0.011)
Dummy of being female
0.001
0.016
-0.028
(0.011)
(0.010)
(0.022)
Age
0.000
0.000
0.000
(0.000)
(0.000)
(0.001)
Intermediate secondary school
0.015
-0.012
-0.007
(Base: less than interm. secon. school)
(0.015)
(0.011)
(0.025)
High school and more
-0.007
0.004
0.017
(0.014)
(0.015)
(0.035)
Living with partner (Base: Married)
0.010
-0.010
-0.049*
(0.025)
(0.013)
(0.028)
Single or not living with partner
-0.009
-0.024*
-0.022
(0.013)
(0.012)
(0.027)
Household income: <2500 Euro
-0.034***
0.003
-0.019
(Base: <1500)
(0.012)
(0.014)
(0.028)
Household income: <3500 Euro
-0.028**
0.015
-0.033
(0.013)
(0.022)
(0.029)
Household income: <5000 Euro
-0.019
0.012
-0.042
(0.015)
(0.027)
(0.032)
Household income: >5000 Euro
-0.009
0.008
-0.003
(0.020)
(0.030)
(0.055)
Household size
0.007
-0.026***
-0.011
(0.005)
(0.007)
(0.012)
Dummy of living in east Germany
-0.011
-0.012
-0.053**
(0.012)
(0.011)
(0.021)
Dummy of being foreigner
-0.013
0.005
(0.021)
(0.061)
Length of interview (in minutes)
0.002***
(0.001)
Pr(refusal)
0.045
0.022
0.081
N
1480
716
659
log-likelihood
-278.27
-107.32
-193.79
Prob > χ2crit.
0.581
0.014
0.062
Notes: Model 1 - 3 are estimated using a probit specification and the table reports the marginal effects
of the different variables on refusing to participate in the experiment. Numbers in parentheses denote
the standard error of the marginal effects. Significance levels are denoted :
Source: SOEP 2003, AS03, and AS04
∗ 10%
∗∗ 5%
∗ ∗ ∗ 1%
find that the longer the questionnaire, the less likely it is that participants agree to
participate in the subsequent experiment in AS04.
Furthermore, the interviewer is a possible source of influence on participation in the
experiment. However, we do not find interviewer characteristics such as age, gender and
years of experience to be influential (models 1 - 3 in Table A.3).
Table A.3: Dep = Dummy of whether a person
SOEP03
Years of experience in polling firm
0.001
(0.001)
Dummy for a female interviewer
0.022
(0.014)
Age of the interviewer
-0.000
(0.001)
N
1491
Adjusted R2
0.00
Prob > Fcrit.
0.116
refused to
AS03
-0.000
(0.001)
-0.024
(0.015)
-0.001
(0.001)
729
-0.00
0.229
participate in the experiment
AS04
-0.002
(0.003)
-0.040
(0.044)
-0.002
(0.002)
558
0.00
0.559
Notes: Models 1 - 3 are estimated using a OLS specification and the table
reports the coefficients. Numbers in parentheses denote the standard error
clustered on interviewer level. Significance levels are denoted: ∗ 10% ∗∗ 5%
∗ ∗ ∗ 1%
Source: SOEP 2003, AS03, and AS04
In sum we find that selflessness, reciprocity, and interviewer characteristics do not
matter in the decision to participate in the experiment. A medium income, a large
household, and living in Eastern Germany reduce the probability that a persons will
refuse to participate. Finally the longer the previous questionnaire lasted, the more
likely it is that participants will refuse to participate in the present experiment in the
AS.
A.4
A.4.1
Sensitivity of the experimental design
Design
In the AS02 and the SOEP 2003 we observed that the modus of the distribution of
first movers’ choices is to transfer half of the endowment (36% and 45%). This result
is not an artefact of our specific study design or our instructions since in other studies
half of the endowment is the modal choice as well (e.g., Berg et al. 1995, Bellemare &
Kroeger 2007). The question arises whether the widely observed pattern is dependent
on the design of the trust game. More specifically we ask how the level of trust changes
if we reduce the number of choices and remove the possibility to transfer half of the
endowment. To test this, we run three different experiments. In the SOEP 2003, we
have run the basic experiment with 11 options for a transfer, which run from 0 to 10
points. In the AS03 we reduced the choices to only three transfer options, which were
0, 5, or 10 points. In the AS04 we eliminated the choice for a transfer of 5 points by
allowing only transfer levels of even numbers.
The distributions of transfers in the three experiments are very different by construction (Figure A.1). However, the average transfer was almost the same in the three
experiments. In the SOEP 2003 with 11 options 51.5% of the endowments was transferred, in the AS03 51.3% and in the AS04 50.3% of the endowment was transferred.
These differences are far from being significant.11 The probability of behaving trustworthy is not dependent on how many options subjects have either (Figure A.2 and
regression 1 in Table A.4).
A.4.2
Strategy method
The strategy method is a widely used elicitation procedure in experimental economics.
With this method, second movers are asked to decide for every possible first mover decision. In the trust game, this procedure allows us to distinguish between selfish players
and conditionally cooperative players and between the latter and altruistic players. If
a second mover, for example, receives zero points from the first mover and he/she does
not transfer back, we do not know whether he/she would transfer a positive amount
back if a first mover transferred 5 points. The disadvantage is that it is more complicated to explain to subjects and the incentives are diluted since only one decision will
actually be paid out. A further potential disadvantage is that the conditional decisions
11
Two-sided T-test for differences in the mean with unequal variances: SOEP 03 vs. AS03 t = 0.69
p>0.48; SOEP 03 vs AS04 t = 0.60 p>0.54; AS03 vs. AS04 t = 0.06 p>0.95
41
Figure A.1: Levels of trust: different scales
11 options (SOEP 03)
.6
.5
.4
.3
.2
.1
0
6 options (AS04)
relative frequencies
.6
.5
.4
.3
.2
.1
0
3 options (AS03)
.6
.5
.4
.3
.2
.1
0
0
1
2
3
4
5
6
7
8
9
10
Levels of trust (0=lowest 10=highest) (first mover transfer)
42
Figure A.2: Levels of trustworthiness
1
percentage of trustworthy people
.9
.8
.7
.6
.5
.4
.3
.2
SOEP 03
AS04
AS03
.1
0
0
1
2
3
4
5
6
7
8
9
Level of trust (0=lowest 10=highest) (first mover transfer)
10
are less emotionally arousing than in the situation in which one knows what the other
person decided. To our knowledge no study compares the strategy method and the direct method in a trust game. Previous research on bargaining games has shown that
subjects’ behaviour is different with the strategy method than with the direct method
(e.g., Schotter et al. 1994, Hoffman et al. 1998, Guth et al. 2001, Brosig et al. 2003).
Concerning social dilemma games, the only study that has analysed possible differences
so far is a study by Brandts & Charness (2000). They analyzed a prisoner’s dilemma
and a game of chicken and found no difference. We implemented the strategy method
for the second mover in the AS03. In order not to make it too complicated, we restricted
first movers’ choices to three options, which were 0, 5, or 10 points. In the other AS and
the SOEP 2003 we used the direct method. Thus, we can compare the level of trustworthiness with and without the strategy method. We find no difference in the average
level of trustworthiness between the strategy method and the direct method (regression
2 in Table A.4). For a first mover transfer of 5 (10) points, 91 (50)% of subjects are
trustworthy using the direct method and 88 (56)% using the strategy method12 . If we
12
Two-sided Fisher Exact tests: 5 points p>0.209; 10 points p>0.186
43
analyse the decision of the second mover in more detail, we find a difference in average
return transfers for a first mover transfer of 10 points. With the strategy method, return
transfers are higher than with the direct method (6.0 versus 6.8 points: t-test: t=-2.603
p<0.01). For the other first mover transfers, that are 0 and 5 points, we do not find any
significant difference in return transfers.
Table A.4: Sensitivity of experimental measure of trustworthiness
Dependent variable: dummy of being trustworthy
Dummy for design with 3 options
Dummy for design with 6 options
Design
-0.009
(0.041)
0.027
(0.025)
strategy method
Dummy for strategy method (AS03)
Controlled for first mover transfer
Constant
YES
0.551***
(0.023)
1722
0.126
YES
N
Adjusted R2
Cluster on individual level
Notes: OLS regression with robust standard errors :
Source: AS02, AS03, AS04, and SOEP 2003
44
∗ : 10%
0.010
(0.024)
YES
0.530***
(0.024)
1333
0.160
YES
∗∗ : 5%
∗ ∗ ∗ : 1%